Forecasting needs of the operational activity of a logistics operator

• Autorzy: Kmiecik Mariusz , Wolny Maciej

Identyfikatory

DOI

10.17270/J.LOG.2022.713

• Języki publikacji: EN

Abstrakty

EN

Background: The paper considers the issue of operational needs of logistics operator connected with the implementation of demand forecasting tool in his activity. The aim of this article is to present research results on the ability to meet the expectations of distribution centre managers at the operational level. To achieve the main goal, three research questions concerning general requirements and possibilities of meeting the requirements set by managers working for a logistics operator were also defined and related to operational needs. Methods: The research analysed the operational requirements of a logistics operator using a survey conducted among managers dealing with the operational work that is performed in the operator's warehouses. Then, the possibility of implementing and operating a forecasting tool based on the ARIMA algorithm in the logistics service of a confectionery manufacturer was analysed, providing the verification of usefulness of such a tool and the level of its adjustment to operational requirements. Results: The forecasting tool is especially useful in the operator's activity in order to support the resource planning process of warehouse operation. However, managers set high requirements regarding the verifiability of the operation of such a tool, which is not completely available in the current situation. The article also shows the future development paths of this tool. Conclusions: The article shows possibilities related to the use of a forecasting tool in activities related to the provision of services in contract logistics. This allows for verification of the needs and capabilities of the logistics operator who would forecast the demand to support the operations it carries out.

Słowa kluczowe

EN

3PL; logistics operator; demand forecasting; distribution network

PL

3PL; operator logistyczny; prognozowanie popytu; sieć dystrybucyjna

• Wydawca: Wyższa Szkoła Logistyki
• Czasopismo: LogForum
• Rocznik: 2022
• Tom: Vol. 18, no. 2
• Strony: 197--212
• Opis fizyczny: Bibliogr. 28 poz., rys., tab., wykr.

Twórcy

autor

Kmiecik Mariusz

• Silesian University of Technology, Zabrze, Poland

• https://orcid.org/0000-0003-2015-1132

autor

Wolny Maciej

• Silesian University of Technology, Zabrze, Poland

• https://orcid.org/0000-0002-8872-7794

Bibliografia

• 1. Alevizos, E., Artikis, A., & Paliouras, G., 2018, Wayeb: a tool for complex event forecasting. arXiv preprint arXiv, https://arxiv.org/abs/1901.01826
• 2. Barlas, Y., & Gunduz, B., 2011, Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains. Journal of the Operational Research Society, 62(3), 458-473, https://doi.org/10.1057/jors.2010.188
• 3. Bayraktar E. & Koh S.L., 2007, Gunasekaran, A.; Sari, K.; Tatoglu, E. The role of forecasting on bullwhip effect for E-SCM applications. Int. J. Prod. Econ. 2007, 113, 193-204, https://doi.org/10.1016/j.ijpe.2007.03.024
• 4. Berk, L., Bertsimas, D., Weinstein, A. M., & Yan, J., 2019, Prescriptive analytics for human resource planning in the professional services industry. European Journal of Operational Research, 272(2), 636-641, https://doi.org/10.1016/j.ejor.2018.06.035
• 5. Broberg, T., & Persson, L., 2016, Is our everyday comfort for sale? Preferences for demand management on the electricity market. Energy Economics, 54, 24-32, https://doi.org/10.1016/j.eneco.2015.11.005
• 6. Chu, C. W., & Zhang, G. P., 2003, A comparative study of linear and nonlinear models for aggregate retail sales forecasting. International Journal of production economics, 86(3), 217-231, https://doi.org/10.1016/S0925-5273(03)00068-9
• 7. Croxton, K. L., Lambert, D. M., García-Dastugue, S. J., & Rogers, D. S., 2002, The demand management process. The International Journal of logistics management, 13(2), 51-66, https://doi.org/10.1108/09574090210806423
• 8. Fahrioglu, M., & Alvarado, F. L., 2001, Using utility information to calibrate customer demand management behavior models. IEEE transactions on power systems, 16(2), 317-322, https://doi.org/10.1109/59.918305
• 9. Grzelak M., Borucka M., Buczyński Z., 2019. Forecasting the demand for transport services on the example of a selected logistic operator, Archives of Transport , vol. 52, pp. 81-93. https://doi.org/10.5604/01.3001.0014.0210
• 10. Mentzer, J. T., & Thomas Jr, D. E., 2000, Customer demand planning at Lucent Technologies. Industrial Marketing Management, 29(1), 19-19. https://doi.org/10.1016/S0019-8501(99)00108-X
• 11. Kalantari, M., Rabbani, M., & Ebadian, M., 2011, A decision support system for order acceptance/rejection in hybrid MTS/MTO production systems. Applied Mathematical Modelling, 35(3), 1363-1377, https://doi.org/10.1016/j.apm.2010.09.015
• 12. Kim, M. S., Kim, K. W., & Park, S. S., 2012, A study on the air travel demand forecasting using time series ARIMA-intervention model. Journal of the korean Society for Aviation and Aeronautics, 20(1), 66-75, https://doi.org/10.12985/ksaa.2012.20.1.066
• 13. Kmiecik, M., 2021, Implementation of forecasting tool in the logistics company- case study. Zeszyty Naukowe. Organizacja i Zarządzanie/Politechnika Śląska, https://doi.org/10.29119/1641-3466.2021.152.9
• 14. Kramarz, W., 2013, Modelowanie przepływów materiałowych w sieciowych łańcuchach dostaw: odporność sieciowego łańcucha dostaw wyrobów hutniczych. Difin.
• 15. Kramarz, M., & Kmiecik, M., 2022, Quality of Forecasts as the Factor Determining the Coordination of Logistics Processes by Logistic Operator. Sustainability, 14(2), 1013, https://doi.org/10.3390/su14021013
• 16. Martin, D., Spitzer, P., & Kühl, N., 2020, A new metric for lumpy and intermittent demand forecasts: Stock-keeping-oriented prediction error costs. arXiv preprint, https://arxiv.org/abs/2004.10537 https://doi.org/10.48550/arXiv.2004.10537
• 17. Morris, J. N., Sherwood, S., & Gutkin, C. E., 1988, Inst-Risk II: an approach to forecasting relative risk of future institutional placement. Health Services Research, 23(4), 511.
• 18. Omar, H., Hoang, V. H., & Liu, D. R., 2016, A hybrid neural network model for sales forecasting based on ARIMA and search popularity of article titles. Computational intelligence and neuroscience, 2016, https://doi.org/10.1155/2016/9656453
• 19. Panahifar, F., Heavey, C., Byrne, P. J., & Fazlollahtabar, H., 2015, A framework for collaborative planning, forecasting and replenishment (CPFR): state of the art. Journal of Enterprise Information Management, https://doi.org/10.1108/JEIM-09-2014-0092
• 20. Sohrabpour, V., Oghazi, P., Toorajipour, R., & Nazarpour, A., 2021, Export sales forecasting using artificial intelligence. Technological Forecasting and Social Change, 163, 120480, https://doi.org/10.1016/j.techfore.2020.120480
• 21. Subramanian, L., 2021, Effective demand forecasting in health supply chains: emerging trend, enablers, and blockers. Logistics, 5(1), 12, https://doi.org/10.3390/logistics5010012
• 22. Szozda, N., & Świerczek, A., 2016, Efektywność procesu zarządzania popytem na produkty w łańcuchu dostaw. Zeszyty Naukowe Uniwersytetu Gdańskiego. Ekonomika Transportu i Logistyka, (58 Modelowanie procesów i systemów logistycznych, Cz. 15), 157-175.
• 23. Świerczek, A., 2019, The effects of demand planning on the negative consequences of operational risk in supply chains. LogForum, 15(3), https://doi.org/10.17270/J.LOG.2019.340
• 24. Wacker, J. G., & Lummus, R. R., 2002, Sales forecasting for strategic resource planning. International Journal of Operations & Production Management, https://doi.org/10.1108/01443570210440519
• 25. Wang Ch-N., Day J-D., Nguyen T-K-L., 2018, Applying EBM and Grey forecasting to assess efficiency of third-party logistics providers, Journal of Advanced Transportation , vol.2108, pp.44575, https://doi.org/10.1155/2018/1212873
• 26. Westcott, R., 2004, A scenario approach to demand forecasting. Water Science and Technology: Water Supply, 4(3), 45-56, https://doi.org/10.2166/ws.2004.0042
• 27. Williams, B. D., & Waller, M. A., 2011, Top‐ down versus bottom-up demand forecasts: the value of shared point-of-sale data in the retail supply chain. Journal of Business Logistics, 32(1), 17-26, https://doi.org/10.1111/j.2158-1592.2011.01002.x
• 28. Wolny, M., & Kmiecik, M., 2020, Forecasting demand for products in distribution networks using R software. Zeszyty Naukowe. Organizacja i Zarządzanie/Politechnika Śląska, https://doi.org/10.29119/1641-3466.2020.142.8

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).